Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics

HUI Yang; WANG Yong-gang; PENG Hui; HOU Shu-qian

doi:10.19818/j.cnki.1671-1637.2021.04.016

Volume 21 Issue 4

Sep. 2021

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Article Navigation > Journal of Traffic and Transportation Engineering > 2021 > 21(4): 210-222

HUI Yang, WANG Yong-gang, PENG Hui, HOU Shu-qian. Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 210-222. doi: 10.19818/j.cnki.1671-1637.2021.04.016

Citation:

HUI Yang, WANG Yong-gang, PENG Hui, HOU Shu-qian. Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 210-222. doi: 10.19818/j.cnki.1671-1637.2021.04.016

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Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics

doi: 10.19818/j.cnki.1671-1637.2021.04.016

1.
Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, Xi'an 710064, Shaanxi, China
2.
Transportation Soft Science Research Center, Chang'an University, Xi'an 710064, Shaanxi, China
3.
Xi'an Rail Transit Group Company Limited, Xi'an 710018, Shaanxi, China

Funds:

National Natural Science Foundation of China 52072044

Natural Science Foundation of Shaanxi Province 2021JQ-295

More Information

Author Bio:
HUI Yang(1989-), female, engineer, doctoral student, huiyanghy@chd.edu.cn
Received Date: 2021-03-26
Available Online: 2021-09-16
Publish Date: 2021-08-01

Abstract

Abstract

To improve the accuracy of subway passenger flow prediction, by considering the Xi'an Metro Line 1 as an example, five main factors affecting subway passenger flow variations, such as festival, non-festival, time period, station, and weather, were extracted to analyze the coupled spatial-temporal characteristics of subway passenger flow. A back propagation (BP) neural network was constructed to predict the subway passenger flow. The proposed BP neural network was further optimized by using a particle swarm optimization (PSO) algorithm that introduced adaptive mutation and balanced inertia weights to form a subway passenger flow prediction system that could consider complex influence factors. Transfer stations and non-transfer stations including a first and an intermediate station were selected, the weather, festival, and non-festival factors were considered, and the BP neural network models for different time periods were compared. Then, the prediction errors of the PSO-BP neural network model were optimized. Research results show that by considering the weather, festival and non-festival factors, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the optimized PSO-BP neural network model predictions at transfer stations within the optimized time periods decrease by 40.13%, 31.46% and 23.89%, respectively, compared with the optimized PSO-BP neural network models prediction errors without the time periods, decrease by 17.50%, 17.86% and 17.32% compared with the BP neural network models prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions in the non-transfer stations within the optimized time periods decrease by 16.50%, 20.99% and 32.59%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 11.48%, 12.10% and 17.73%, respectively, compared with the BP neural network model prediction errors within the optimized time periods. The MAE, RMSE, and MAPE of the optimized PSO-BP neural network model predictions at each station within the optimized time periods decrease by 24.37%, 24.48% and 29.69%, respectively, compared with the optimized PSO-BP neural network model prediction errors without time periods, and decrease by 13.49%, 14.02% and 17.59%, respectively, compared with the BP neural network model prediction errors within the given time periods. Therefore, using the optimized PSO-BP neural network model and considering the influencing factors can improve the accuracy of subway passenger flow prediction. 8 tabs, 12 figs, 30 refs.
- urban rail transit,
- passenger flow prediction,
- coupled spatial-temporal characteristic,
- back propagation neural network,
- particle swarm optimization algorithm,
- adaptive mutation,
- inertia weight

YU Qiang(1961-), male, professor, PhD, qiangyu@chd.edu.cn.

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References(30)

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Subway passenger flow prediction based on optimized PSO-BP algorithm with coupled spatial-temporal characteristics

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